Systems and methods for fine motor control of fingers on a prosthetic hand to emulate a natural stroke

ABSTRACT

The present invention generally relates to a system and method for fine motor control of fingers on a prosthetic hand. In particular, the present disclosure describes a system and method for controlling the flexion or extension of one or more fingers of a prosthetic hand to reproduce a natural stroke such as for, e.g., writing, painting, brushing teeth, or eating. The systems and methods described herein use electromyographic (EMG) signals and, more particularly, combinations of electromyographic signals, from muscles in the forearm to activate one or more motors of the prosthetic hand that control the motion of the prosthetic fingers. The electromyographic signals may be used to cause fingers of a prosthetic hand to, for example, imitate a writing stroke while the fingers of the prosthetic hand hold a writing utensil. Additionally, the present invention describes electrode placement locations that maximize peak signal detected while maintaining a low base-line signal.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority under 35 U.S.C. § 119(e)to U.S. Provisional Patent Application No. 62/341,395, filed on May 25,2016, entitled “Prosthetic Hand,” which is hereby incorporated byreference in its entirety.

FIELD OF THE INVENTION

The present invention generally relates to a system and method for finemotor control of fingers on a prosthetic hand. In particular, thepresent disclosure describes a system and method for controlling theflexion or extension of one or more fingers of a prosthetic hand toreproduce a natural stroke such as for, e.g., writing, painting,brushing teeth, or eating. The systems and methods described herein useelectromyographic (EMG) signals and, more particularly, combinations ofelectromyographic signals, from muscles in the forearm to activate oneor more motors of the prosthetic hand that control the motion of theprosthetic fingers. The electromyographic signals may be used to causefingers of a prosthetic hand to, for example, imitate a writing strokewhile the fingers of the prosthetic hand hold a writing utensil.Additionally, the present invention describes electrode placementlocations that maximize peak signal detected while maintaining a lowbase-line signal.

BACKGROUND

Motion of the human body is generally controlled by the contraction ofvarious muscles. Muscular contraction is caused when electrical signals(i.e., “action potentials”) travel from the brain, through the centraland peripheral nervous systems, and into the target muscle tissue toeffect contraction of structural units within the muscle tissue—known assarcomeres. For example, motion of the fingers of the human hand iscontrolled by several muscles in the forearm. These forearm musclescontract when electrical signals are sent from the brain, through thecentral nervous system (i.e., the spine), through the peripheral nervoussystem (i.e., the arm), and into the muscle tissue to trigger thecontraction of the sarcomeres within the muscle tissue. The forearmmuscles are connected to the bones of the fingers via tendons such thatwhen the forearm muscles contract, the fingers bend.

To generate an “action potential,” a gradient of ions creates a voltagedifference across an axon of a neuron. When a threshold voltagedifference is exceeded due to the ion gradient, an electrical wavepropagates down the length of the axon until it reaches the end of theaxon, which may be within muscle tissue. The neuron releasesacetylcholine and transfers the action potential to the muscle tissue.The electrical signal will travel through the tissue and trigger thecontraction of the individual sarcomeres. One synapse generally controlsan entire muscle fiber. One motor neuron usually controls severaladjacent muscle fibers. A group of fibers under the control of a singlemotor neuron is known as a motor unit.

Prosthetic devices have long been used to replace missing body parts,such as hand, arms, and legs. However, these prosthetic devices may beimmobile and thus lacking in the same utility that a natural body partimparts. Other prosthetic devices known in the art may use varioussensors (e.g., electrodes) to detect the action potential propagationthrough a muscle—also called electromyography—and use the detection ofthe action potential as an input signal to control motors that allow theprosthetic device to move. In an example, a prosthetic hand may includeone or more motors that control the flexion of individual fingers tomimic the natural flexion of the fingers of a biological hand.

One example of a prosthetic hand known in the art is the Dextrus hand ofthe Open Hand Project. The Dextrus hand is a prosthetic hand that offerssome of the functionality of a human hand by using electric motorsinstead of muscles and steel cables instead of tendons all packagedwithin a polymer housing. The electric motors are controlled byelectronics (such as a microprocessor) that utilize stick-on electrodesto read signals from the muscles in the forearm, which can control thefingers of the prosthetic hand, causing it to open or close. However,previous implementations of the Dextrus hand were only capable ofgripping objects and did not have the capability of fine motor controlof the fingers to generate a stroking motion. Thus, a user of previousimplementations of the Dextrus hand would have to grip an object and usebody parts outside of the prosthetic (e.g., shoulder and/or elbow) tocreate motion of the object being held. This is inconvenient for theuser when performing tasks that would more suitably be performed usingfine motor control of the fingers.

Other known prosthetic hands include the i-limb, which is an externallypowered prosthesis often controlled by myoelectric signals. However, thei-limb only has preprogrammed grips and does not have the capability offine motor control of the fingers to generate a stroking motion. A userwould have to grip an object with a preprogrammed grip and use jointsand/or muscles outside of the finger joints/muscles (such as theshoulder and/or elbow) to create motion of the object being held.Another prosthetic hand, the bebionic3, similarly has preprogrammedgrips that are used to hold an object, but does not have the capabilityof fine motor control of the fingers to generate a stroking motion.Again, a user would have to grip an object with a preprogrammed grip anduse joints and/or muscles outside of the finger joints/muscles (such asthe shoulder and/or elbow) to create motion of the object being held.

While advances in prosthetic technology have allowed these devices to bemanufactured efficiently at low cost, the motion of these prostheticdevices can be difficult to control. Among other issues with control ofthe prosthetics, no prosthetics have yet addressed the issue of finemotor control of the fingers to generate a stroking motion. Such amotion may be useful for everyday tasks such as writing, painting,eating, brushing teeth, etc. to allow a user to have a more normal lifepost-amputation of an appendage. Thus, a need exists for systems andmethods of controlling prosthetic devices in a way that mimics the finemotor control of the body part the prosthetic intends to replace. Withrespect to a prosthetic hand, for example, a need exists for systems andmethods for fine motor control of the fingers to allow for delicatemotion of the fingers, such as forming strokes with a writing utensil.

SUMMARY OF THE INVENTION

In an embodiment, a prosthetic hand includes a hand body, at least twofingers, a controller that utilizes a combination of neuronal signalsduring a controlled motor activity to determine a threshold. Thecontroller may move at least one finger when the threshold has beenexceeded to effect a stroking motion of the at least one finger.

In an implementation, the prosthetic hand further comprises an electrodeplaced on the surface of the skin, and the combination of neuronalsignals may be determined from the electrode. In an implementation, theelectrode may be placed on any suitable location on the body whereneuronal signals can be detected. The electrode may be placed on aforearm. In an implementation, the combination of neuronal signals maybe determined from an implantable device.

In an implementation, the prosthetic hand further comprises a tool heldby the at least one finger, and the stroking motion is a stroke of thetool in which surpassing the threshold activates the at least onefinger, directly causing the stroking motion. In an implementation, thetool may be a writing utensil. In an implementation, the controllercauses the at least one finger to move between a first, inactivatedposition to a second, activated position. In an implementation, thecontroller returns the at least one finger to the first, inactivatedposition after the stroking motion is completed.

In an implementation, the combination of neuronal signals is a ratio ofa peak neuronal signal of a muscle during a controlled motor activity toa baseline signal of the muscle without any motor activity. In animplementation, the peak neuronal signal is an average peak signal andthe baseline signal is a standard deviation of a previously detectedbaseline neuronal signal.

In an embodiment, a prosthetic appendage includes an appendage body, atleast one digit, and a controller that utilizes a combination ofneuronal signals during a controlled motor activity to determine athreshold. The controller may move the at least one digit when thethreshold has been exceeded to effect a writing motion of the at leastone digit.

In an embodiment, a method of operating a prosthetic hand includedetermining a baseline neuronal signal of a muscle without any motoractivity, determining an elevated neuronal signal of the muscle during acontrolled motor activity, computing a threshold based on a combinationof the elevated neuronal signal and the baseline neuronal signal suchthat discrimination between intentional and unintentional musclecontractions can be determined, and facilitating activation of theprosthetic hand when the threshold has been exceeded to effect a writingmotion of at least one finger on the prosthetic hand.

In an implementation, the method may include placing electrodes on thesurface of the skin and the baseline neuronal signal and the elevatedneuronal signal are determined by the electrodes. The electrodes may beplaced on the surface of the skin of a forearm. In an implementation,the baseline neuronal signal and the elevated neuronal signal aredetermined from an implantable device.

In an implementation, the stroking motion generates a stroke of a toolin which surpassing the threshold activates the at least one finger,directly causing the writing motion. In an implementation, the tool is awriting utensil. In an implementation, the activation of the prosthetichand causes the at least one finger to move between a first, inactivatedposition to a second, activated position. In an implementation, themethod further comprises returning the at least one finger to the first,inactivated position after the stroking motion is completed.

In an implementation, the threshold is further based on asignal-to-noise ratio of the elevated neuronal signal and the baselineneuronal signal. In an implementation, the method further comprisescomputing summary statistics of the baseline neuronal signal. In animplementation, computing statistics of the baseline neuronal signalconsists of computing the standard deviation of the baseline neuronalsignal.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects and advantages will be apparent uponconsideration of the following detailed description, taken inconjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout.

FIG. 1A shows an isometric view of an exemplary prosthetic hand.

FIG. 1B shows a top view of an exemplary prosthetic hand.

FIG. 1C shows a bottom view of an exemplary prosthetic hand.

FIG. 1D shows an isometric view of an exemplary prosthetic hand holdinga pencil in a non-activated state.

FIG. 1E shows an isometric view of an exemplary prosthetic hand holdinga pencil in an activated state during a writing stroke motion.

FIG. 2A shows an illustration of musculature and flexor carpi ulnariselectrode placement.

FIG. 2B shows an illustration of musculature and brachioradialiselectrode placement.

FIG. 2C shows an illustration of musculature and flexor carpi radialiselectrode placement.

FIG. 3A shows a graph representing the electromyography of the flexorcarpi ulnaris.

FIG. 3B shows a graph representing the electromyography of thebrachioradialis.

FIG. 3C shows a graph representing the electromyography of the flexorcarpi radialis.

FIG. 4A shows a graph of average flexor carpi ulnaris EMG signal foreach finger.

FIG. 4B shows a graph of average trial-by-trial difference in flexorcarpi ulnaris EMG signal for each finger.

FIG. 5A shows a graph of average brachioradialis EMG signal for eachfinger.

FIG. 5B shows a graph of average trial-by-trial difference inbrachioradialis EMG signal for each finger.

FIG. 6A shows a graph of average flexor carpi radialis EMG signal foreach finger.

FIG. 6B shows a graph of average trial-by-trial difference in flexorcarpi radialis EMG signal for each finger.

FIG. 7 shows a flow chart of a method according to an embodiment of theinvention.

DETAILED DESCRIPTION

A prosthetic hand of the invention generally includes a housing, one ormore fingers, and one or more motors connected to the fingers viacables. In an embodiment, in place of cables, other means may be used toeffect flexion of the finger. In an example, gears may be connected to amotor to effect flexion or extension on a finger. The motors may becontrolled by a controller located within or exterior to the prosthetichousing, such as a processor (e.g., an Arduino or other suitablemicroprocessor) to effect motion of the fingers. The prosthetic hand mayfurther include stick-on electrodes in communication with thecontroller. In another embodiment, other suitable data input methods asare known in the art may be used to control the prosthetic hand. Theelectrodes may be placed on the surface of the skin to detect electricalsignals from the voluntary contraction of muscles. In one embodiment,the electrodes are placed on the surface of the skin of the forearm todetect electrical signals from the muscles in the forearm that controlthe flexion of the fingers. One skilled in the art will recognize thatany suitable muscle of the body may be used in place of the forearmmuscles to control the prosthetic hand. For example, if no forearmmuscles were available, such as when the forearm was amputated, musclesin the upper arm may be utilized to provide an input signal to theelectrodes for the controller.

The placement location(s) of the one or more electrodes may correspondto the locations of the muscles that effect flexion of the fingers orother motion of the hand. For example, muscles that may provide usefulelectrical signals may be any of the following: flexor carpi ulnaris,palmaris longus, flexor carpi radialis, pronator teres, brachioradialis,extensor carpi radialis longus and brevis, extensor digitorum, extensordigit minimi, extensor carpi ulnaris, and aconeus. The electrode may beplaced on the skin as a “stick-on” electrode or, alternatively, may beimplanted underneath the skin. If the electrodes are implanted, theelectrodes may be implanted against the muscle from which it is intendedto detect an electrical signal.

To effect motion of the fingers of the prosthetic hand, a baselinesignal may be detected by placing one or more electrodes on the surfaceof the skin of, e.g., the forearm, while the underlying muscles are in arelaxed state (i.e., without any motor activity). The baseline signalmay be recorded over a period of time to determine an average baselinesignal and to compute summary statistics (e.g., standard deviation,mean, skewdness, or kurtosis) of the baseline signal. Exemplary timeperiods may be 5 seconds, 10 second, 15 seconds, and 30 seconds. One ofskill in the art will recognize that the baseline signal may be recordedfor any suitable time period to characterize the baseline signal.Additionally, a baseline difference signal (BDS) may be computed bysubtracting the n^(th) baseline signal point from the (n+1)^(th)baseline signal point for k samples. The equation for baseline signal isdefined below as Equation 1. Summary statistics (e.g., standarddeviation) may be calculated for the baseline difference signal.

$\begin{matrix}{\mspace{214mu}{{{BDS}(n)} = {( {x_{n + 1} - x_{n}} )❘}}} & ( {{Eqn}.\mspace{14mu} 1} )\end{matrix}$

A peak signal may also be detected with a similar setup to thatdescribed above for detecting the baseline signal. In this case, themuscles underlying the electrodes may perform a controlled motoractivity, such that the muscles are in a contracted state. In anexample, one or more fingers of the biological hand may squeeze a springhaving a specific, fixed force required to compress the spring (e.g., 1Newton, 2 Newtons, or 5 Newtons). To detect the peak signal, the one ormore fingers may hold the spring compressed for a suitable period oftime as described above. A peak signal may be recorded from thecontraction of the underlying muscles for the specified period of time.Similar to the baseline signal, an average peak signal and summarystatistics (e.g., standard deviation, mean, skewdness, or kurtosis) maybe computed using the recorded peak signal. Additionally, a peakdifference signal (PDS) may be computed by subtracting the n^(th) peaksignal point from the (n+1)^(th) peak signal point for k samples. Theequation for peak signal is defined below as Equation 2. Summarystatistics (e.g., standard deviation) may be calculated for the peakdifference signal.

$\begin{matrix}{\mspace{205mu}{{{PDS}(n)} = {( {x_{n + 1} - x_{n}} )❘}}} & ( {{Eqn}.\mspace{14mu} 2} )\end{matrix}$

A threshold may generally be calculated from any suitable combination ofEMG signals. In one embodiment, a threshold may be calculated from thebaseline signal and the peak signal to distinguish between an “intended”user motion and an “unintended” user motion. A recordedelectromyographic signal that exceeds the threshold indicates anintended user motion while any recorded signal that does not exceed thethreshold indicates that no motion is intended. The threshold may becalculated as a ratio of a peak signal value to a baseline signal value.In particular, the ratio may be calculated as an average peak differencesignal divided by a standard deviation of the baseline differencesignal. The threshold may be a signal-to-noise ratio (SNR). The equationfor the threshold is defined below as Equation 3.

$\begin{matrix}{{Threshold} = \frac{{PDS}_{avg}}{\sigma_{BDS}}} & ( {{Eqn}.\mspace{14mu} 3} )\end{matrix}$

Once a threshold is determined, the controller may effect motion of theprosthetic hand once this threshold is exceeded. Thus, the method of thepresent invention effectively acts as a filtering method to filter outelectromyographic noise and/or other muscle activity that is not anintended voluntary motion.

In an embodiment, the controller 130 is configured to perform a method700 of operating a prosthetic hand, as shown in FIG. 7. The methodincludes determining 710 a baseline neuronal signal of a muscle withoutany motor activity, determining 720 an elevated neuronal signal of themuscle during a controlled motor activity, computing 730 a thresholdbased on a combination of the elevated neuronal signal and the baselineneuronal signal such that discrimination between intentional andunintentional muscle contractions can be determined, determining 740 thethreshold has been exceeded, and facilitating 750 activation of theprosthetic hand when the threshold has been exceeded to effect a writingmotion of at least one finger on the prosthetic hand.

In an example, the controller may effect motion of one or more fingersto create a stroke of a utensil. The motion and/or activation of the oneor more fingers occurs after the utensil is being gripped. The utensilmay be, e.g., a writing, drawing, painting, eating utensil, a toothbrushfor brushing teeth or any other suitable tool or implement. Inparticular, the controller may cause the one or more motors housedwithin the prosthetic to activate (i.e., rotate) at specified speeds togenerate a fluid writing stroke motion with the one or more fingers(e.g., three fingers as shown in FIGS. 1D and 1E) contacting the writingutensil. The fluid writing stroke motion of the one or more fingers maycreate a linear stroke at a distal end of the writing utensil, similarto a writing stroke made by a biological hand when writing. After theone or more motors cause the fingers to move to an activated position(FIG. 1E), the controller then may cause the motors to activate in theopposite direction to cause the fingers to return to their startingposition (i.e., inactivated position, shown as FIG. 1D). Thus, theprosthetic hand of the present invention may be used to allow users towrite in a similar fashion as with a biological hand by generating afluid writing stroke motion with one or more fingers of a prosthetichand.

One skilled in the art will recognize that these techniques forcontrolling the motion of a prosthetic hand can be used in any suitableprosthetic, such as, for example, a prosthetic foot, leg, or arm.Moreover, the techniques, methods, and systems described herein can beapplied in other suitable applications, such as exoskeletons, orindustrial applications, such as for industrial robots. In each case,one skilled in the art will recognize that a different muscle or set ofmuscles may be used to provide the input signal to the prostheticcontroller and to effect motion of the prosthetic or other device.

FIGS. 1A-1C show an exemplary prosthetic hand 100 of the presentinvention. Prosthetic hand 100 includes a housing 102 having fingers 104a-104 e. Each of fingers 104 a-104 includes at least two joints thatallow the respective finger to move just like a biological hand. Theprosthetic hand 100 further includes cables 106 a-106 d that act as“artificial tendons” and each cable is connected to a motor 110 (shownin FIG. 1A) disposed within the housing 102. The motor 110 may becontrolled by a controller 130 located within or exterior to theprosthetic housing of 102 to effect motion of the prosthetic hand 100.The cables may be made of a metal, such as steel, titanium, or anysuitable metal alloy as is known in the art. Alternatively, the cables106 a-106 d can be made of a polymer. Furthermore, the housing ispreferably made of a polymer, such as polyethylene, polypropylene,polyethylene terephthalate, ABS, or any other suitable polymer orcombination of polymers as is known in the art.

FIGS. 1D and 1E shows an isometric view of an exemplary prosthetic hand100 holding a pencil. In FIG. 1D, the prosthetic hand 100 is gripping awriting utensil (e.g., a pencil or pen) 108 with three (3) fingers 104c-104 e in a non-activated state, which corresponds to the middle finger104 c, index finger 104 d, and thumb 104 e. One of skill in the art willrecognize that any suitable number of fingers may be used to grip thepencil. During detection of electric signals from the muscles, uponexceeding the threshold (as described in more detail above), thecontroller causes the three fingers 104 c-104 e gripping the writingutensil 108 to generate a stroke of the writing utensil 108. The strokemay be a linear stroke of a writing utensil or any other suitable strokethat a user would naturally perform while writing or painting, forexample. FIG. 1E shows an activated state of the prosthetic hand afterthe fingers have performed a writing stroke motion. The writing strokemotion may be repeated over and over again to simulate natural writingby causing the muscles near the electrodes to contract and generate asignal that exceeds the specified threshold.

In the non-activated state shown in FIG. 1D, the writing utensil 108 isangled slightly above a horizontal and the thumb 104 e is supporting thebottom of the writing utensil 108. The thumb 104 e is not bent at thejoints and is substantially straight. The middle finger 104 c is alsosubstantially straight (i.e., the middle finger is not bent at any ofthe joints) and supports a side of the writing utensil 108. The indexfinger 104 d is curled such that a proximal joint of the index finger104 d in FIG. 1D is higher than the same proximal joint in FIG. 1E. Theindex finger 104 d also includes a bend at a distal joint (between adistal segment and a middle segment of the index finger 104 d) such thata distal segment of the index finger 104 d slopes downwardly more thanthe middle segment of the index finger 104 d.

In the activated state shown in FIG. 1E, the writing utensil 108 is atan angle slightly below the horizontal. The thumb 104 e is angled suchthat the proximal segment of the thumb 104 e is substantially horizontaland the joint of the thumb 104 e is now angled such that the distalsegment of the thumb 104 e is substantially the same slope as the entirethumb 104 e in the non-activated state of FIG. 1D. The fingertip andsome of the finger pad of the thumb 104 e now support the writingutensil 108. The proximal segment of the index finger 104 d is nowangled further downwardly. The angle created at the distal joint of theindex finger 104 d is larger, since the finger is almost completelystraight. The middle segment of the index finger 104 d angleddownwardly. However, the distal joint of the index finger 104 d hassubstantially no bend, as the middle segment and the distal segment noware substantially straight. A proximal segment and a middle segment ofthe middle finger 104 c both remain substantially horizontal with oneanother (i.e., there is no bend at the distal joint of the middle finger104 c). However, a distal joint of the middle finger 104 c has adownward bend, such that the combined middle segment and distal segmentof the middle finger 104 c are now angled slightly downwardly to followthe motion of the writing utensil 108.

FIGS. 2A-2C show an illustration of musculature electrode placement. Inparticular, FIG. 2A shows the placement of electrodes 202 a, 202 b,which are positioned to detect electrical signals from the flexor carpiulnaris. FIG. 2B shows the placement of electrodes 204 a, 204 b, whichare positioned to detect electrical signals from the brachioradialis. Inanother embodiment, the electrodes 204 a, 204 b may be positioned toadditionally detect electrical signals from the extensor carpi radialis.FIG. 2C shows the placement of electrodes 206 a, 206 b, which arepositioned to detect electrical signals from the flexor carpi radialis.In another embodiment, the electrodes 206 a, 206 b may be positioned toadditionally detect electrical signals from the pronator teres.

FIG. 3A shows a graph representing the electromyography of the flexorcarpi ulnaris. FIG. 3B shows a graph representing the electromyographyof the brachioradialis. FIG. 3C shows a graph representing theelectromyography of the flexor carpi radialis.

FIG. 4A shows a graph of average flexor carpi ulnaris EMG signal foreach finger. FIG. 4B shows a graph of average trial-by-trial differencein flexor carpi ulnaris EMG signal for each finger. As can be seen inFIGS. 4A and 4B, a peak appears shortly after 5000 ms representing thetime at which the subject voluntarily activated the specific finger tocompress a spring scale. The data from FIGS. 4A and 4B suggest that theflexor carpi ulnaris may be a candidate for electromyographic thresholddetection for activating a prosthetic to generate motion.

FIG. 5A shows a graph of average brachioradialis EMG signal for eachfinger. FIG. 5B shows a graph of average trial-by-trial difference inbrachioradialis EMG signal for each finger. FIGS. 5A and 5B shows anoisy signal for each finger and no discernible peaks for more than oneparticular finger.

FIG. 6A shows a graph of average flexor carpi radialis EMG signal foreach finger. FIG. 6B shows a graph of average trial-by-trial differencein flexor carpi radialis EMG signal for each finger. FIGS. 6A and 6Bshows discernible peaks for some fingers but is generally considered anoisy signal. The data from FIGS. 6A and 6B suggest that the flexorcarpi radialis and/or the pronator teres may be a candidate forelectromyographic threshold detection for activating a prosthetic togenerate motion.

Experimental tests were performed to characterize EMG signals fromvarious muscles and compute an appropriate threshold that indicateswhether a user intends to generate a writing stroke motion. Theexperimental setup and results are discussed in more detail below.

The aim of this research was to quantify and characterize theelectromyographic potential of several forearm muscles during acontrolled motor activity. As this information will be used to develop acontrol algorithm for a neuroprosthetic hand (prototype shown above asFIGS. 1A-1E), a further goal was to find electrode placements thatprovide a consistently low baseline signal and consistently high peaksignal, allowing for clear discrimination between intentional andunintentional muscle contractions. Ultimately, upon exceeding apredetermined threshold, the activation of prosthetic movement will beinitiated.

Methods: Surface EMG recordings were made from three muscles, the flexorcarpi ulnaris, flexor carpi radialis and brachioradialis, of the rightforearm of a single subject. For each trial of a specific motor task,the subject remained still (i.e., having no motor activity in the targetmuscles) for 5 seconds to obtain a baseline EMG reading. The subjectthen flexed a finger over a 1 second period to apply a force of 2Newtons against a spring scale. This force was sustained for 5 seconds,after which the subject relaxed the finger over a 1 second period,returning to the original, relaxed position for an additional 5 seconds.This procedure was repeated five times for each digit on right hand,giving a total of 25 trials per muscle. The average and standarddeviation of the EMG signal for each digit/muscle combination was thencalculated. The difference of the EMG signal value at each timepoint andthe preceding time point was also determined. Finally, thesignal-to-noise ratio (SNR) was calculated for each digit/musclecombination by taking the average peak difference value and dividing itby the standard deviation of the corresponding baseline differencevalue.

Results: Of the 75 trials (25 per muscle×3 muscles), all successfullyyielded EMG recordings. For the flexor carpi ulnaris, peakscorresponding to the onset of motor activity were evident for all fivedigits (FIG. 3A). Such peaks were largely absent for the brachioradialismuscle (FIG. 3B) and were of lower magnitude for the flexor carpiradialis muscle (FIG. 3C). Motor activation by the thumb yielded thehighest SNR value (4.6) for the flexor carpi radialis; the lowest SNRvalue (2.3) was associated with the little finger.

Conclusions: Results indicated that the flexor carpi ulnaris muscleconsistently yielded the clearest EMG signal associated with deliberateonset of muscle activity, and is an excellent candidate for use inneuroprosthetics.

This research is a stepping-stone toward successfully implementing alow-cost prosthetic hand optimized for efficient handwriting throughfinger movement. Natural manual handwriting requires a complex set offinger motions that have not been adequately addressed in prostheses atthis time in prosthetics currently on the market. However, therelatively recent development of consumer grade 3D printer technologyhas the potential to revolutionize the prosthetics world, opening upboth development of and access to these critical medical devices. Whilecurrent low-end prosthetic hands may generally feature the ability togrip a pencil firmly for writing, movement of the gripped object (e.g.,a pencil) needs to be done outside of the prosthetic (e.g., with ashoulder and/or elbow). All of the necessary movements for writing aregenerated from body parts outside the hand itself in current models ofprosthetics. Amputees instead rely on their elbows and shoulders toproduce the fine strokes required to draw lines and curves,significantly limiting their legibility, speed, and endurance. This isinconvenient for a user and does not lend to a high fidelity prostheticas these low-end prosthetics cannot generate stroking motions used ineveryday life such as those motions used in writing or painting.

The long-term goal of this project is thus to provide amputees withanother degree of freedom when writing, specifically fine motor controlof the fingers. Such a development would allow for more efficient andaccurate movements, enabling faster and more natural writing. Moreover,improved prosthetic dexterity would help reduce the strain on arm andshoulder muscles associated with using them for unnaturally fine motortasks, improving the comfort of the user.

Although highly dexterous designs have been developed in recent years,myoelectric prosthetics capable of intricate movements and that respondto users' muscle activity as inputs are often prohibitively expensive,in the range of $20,000 to $100,000. Even with these highly dexterousdesigns, none have the capability of forming a natural stroke such asthe motion used when writing or painting. While the majority of peoplecannot afford these advanced prosthetics, the advent of 3D printing andversatile small electronics has the potential to bring a similar degreeof technological sophistication to the masses. This technology has thepotential to reduce the cost on such a device to orders of magnitudebelow what is currently on the market. For instance, the plasticnecessary to print a prosthetic hand only costs around $5-10.

Similarly, small electronic devices designed to record electricalactivity in human muscles (i.e., electromyographs) can be obtained forunder $100, and can enable the kinds of direct muscular controlcurrently only available in the most expensive prosthetics. Additionalmaterials such as motors and signal processing equipment are also nowextremely affordable, with the total estimated cost for a 3D printedmyoelectric prosthetic hand in the neighborhood of $200-$250.

In addition to the cost benefits, customized, high-end 3D printedprosthetics have the potential to alleviate much human suffering andimprove quality of life. For amputees, a major determinant of quality oflife is independence and the ability to perform everyday activitiesnormally or with minimal adaptation. One such domain is writing, whichmost people perform daily and, for those with biological fingers,entails a series of highly complex motions. Consequently, amputees mayfeel substantially more “normal” if they are able to produce fluentwriting with naturalistic finger movements. Bringing such advances tothose of low socioeconomic status is particularly critical, as they areoften the most adversely affected by the social and economic hardshipsof disfigurement and amputations. Thus, a prosthetic hand of the presentinvention may be affordable to low-income individuals and/orimpoverished nations to allow those with a hand amputation or otherdebilitating condition of the hand to communicate through writing.

In line with these goals, this experiment represents the first stepstoward achieving a low-cost myoelectric prosthetic hand specialized forwriting by characterizing the electric activity of several muscles thatwill ultimately control the movement of prosthetic fingers. It washypothesized that 1) the EMG recording setup would be able todistinguish intentional motor activity from the background level ofactivity for each muscle tested, but 2) the muscles would vary in howclearly this intentional motor activity could be identified.

The general experiment was as follows: 1) electrodes were placed onforearm muscle groups of interest during a spring pulling task; 2) atregular intervals, the EMG recorded the amplitude of electric potentialcaused by neuromuscular activity through these electrodes; 3) usingcustom code (optionally, in combination with open source code), theprocessing unit received analog signals from the EMG and translated theminto digital potential readings; 4) these readings were recorded andsaved to file for later analysis; and 5) these recordings were analyzedand characterized. The findings from this experiment informed thedevelopment of algorithms for controlling prosthetic fingers to emulatemanual handwriting by directly interfacing with forearm musculature.

EMG Setup: Electrodes for the EMG were placed on three muscles, asdepicted in FIGS. 2A-2C. While the recordings were clearly made from thesuperficial muscles of the forearm, it was difficult to determineprecisely which of these muscles were targeted for each recording. Thethree most likely candidates were selected based on anatomical landmarkson the surface of the arm and anatomical reference data. These were theflexor carpi ulnaris (FIG. 2A); the brachioradialis (FIG. 2B); and theflexor carpi radialis (FIG. 2C). For each site, the positive electrodewas placed on the center of the muscle, the negative electrode wasplaced at the distal edge of the muscle, and a ground electrode wasplaced on the bony protuberance of the elbow. Once placed, theelectrodes were connected to the EMG module, which was in turn connectedto the Arduino control module and tested before proceeding.

Writing Force Estimation: In a separate experiment, two test subjectseach applied pressure with pencil in hand to a piece of paper on top ofa triple beam balance. The subjects then simulated writing on the paper;the mass needed to balance out this writing force was used to calculatethe approximate force used when writing. This yielded a mass reading of200±5 grams for both test subjects, corresponding to a force ofapproximately 2 N; this value was subsequently used in the Motor Task(see below).

Recording Setup: Upon proper setup and documentation regarding to thelocation of the electrodes, a spring scale was hung from a woodenstabilizer attached to a table. For each recording site, each of thefingers (i.e., thumb, index, middle, ring, and little finger) waslatched onto the spring scale one at a time. Recordings were initializedby uploading code (modified from code provided by the EMG manufacturers)to the control module; simultaneously, a stopwatch was started. Theuploaded code allowed the electric potential changes detected by the EMGto be quantitatively measured and recorded; the output from this setupwas in the form of electric potential (mV) and time since start ofrecording (ms). These times were crosschecked against the time displayedon the stopwatch to verify accuracy.

Motor Task: For each trial, the test subject was instructed to remain asstill as possible for five seconds to obtain a steady baseline EMGreading. After five seconds, the test subject was instructed to bend thefinger over a 1 second period to reach a reading of 2 Newton on thespring scale. The subject was instructed to maintain this 2 Newton forcein this position for 5 seconds. The process was then reversed, with thesubject relaxing the finger over a 1 second period until the springscale read 0 Newtons. The subject remained in this position for anadditional 5 second, after which the recording was terminated and savedto file. For each EMG site, this procedure was repeated five times perfinger, giving a total of twenty-five trials per recording site; threesites were tested in this manner (flexor carpi ulnaris, brachioradialis,and flexor carpi radialis; see above).

Data Analysis: The EMG data for each trial arrived in the form of anelectric potential readout with an initial sample around 130 ms afterrecording was initialized, then additional samples at regular intervalsset to once every 500 ms starting at 1,700 ms. To quantify thesensitivity of the EMG to changes in neuromuscular activity, thedifference between each reading and the preceding reading was alsocalculated. The “peak” change in neuromuscular activity was thereforedefined as the largest reading-by-reading difference value within a2,000 ms window centered around 5,200 ms, to account for variation inthe timing of the subject. This corresponded to the closest measurementtime to the target muscle activity onset time of 5 seconds. For eachfinger and each muscle site, the reading times were then shifted suchthat the difference peaks were aligned, as these varied somewhat betweentrials. The “baseline” epoch was then defined as the readings thatpreceded the determined peak time point for each trial. Thus, foursummary statistics were generated: average baseline EMG reading, averagepeak EMG reading, average baseline reading-by-reading difference, andaverage peak difference. Standard deviations were also calculated foreach of these measures to quantify their variance.

Initial analysis revealed that in some trials, no discernible peakdifference was apparent. To rigorously define which trials should beexcluded based on an absent peak, a criterion was devised to exclude anytrial in which the peak difference value (defined above) was less thanone standard deviation from the baseline difference average. In thesecases, no alignment was carried out and the baseline epoch was definedas time points up to and including 5,200 ms. In cases where none of thefive trials yielded discernible peaks, no peak value is reported.Additionally, the initial (i.e., 130 ms) reading would occasionallydiverge conspicuously from the other baseline values, and would alsosometimes appear as two different values within approximately 5 ms. Inthe latter case, the first value was disregarded. To avoid skewing ofthe baseline by inaccurate initial readings, any initial values morethan two and a half standard deviations from the baseline average werealso excluded.

In order to compare the sensitivity of each muscle and fingercombination relative to the background level of variation, thesignal-to-noise ratio (SNR) was calculated for each. This wasaccomplished by taking the average peak difference value (i.e. the“signal” of interest) and dividing it by the standard deviation of thecorresponding baseline difference value (i.e., the “noise”). The SNRvalue gives an indication of how easy it will be for a classifieralgorithm to detect a deliberately triggered muscle onset while ignoringrandom fluctuations in the signal. A larger SNR means unintentionalnoise is less likely to be classified as an onset (i.e., a falsepositive) and the detection threshold can be set to a higher valuerelative to the baseline; the higher this threshold, the less likely thesignal is to be missed as an onset (i.e., a false negative).

Of the 75 trials (25 per muscle×3 muscles), all successfully yielded EMGrecordings. The flexor carpi ulnaris EMG summary statistics are listedin Table 1. Peaks corresponding to the motor task were evident for allfive fingers, as seen in the plots of EMG signal values for each trial(FIG. 4A) and calculated reading-by-reading differences (FIG. 4B). Thebrachioradialis EMG summary statistics are listed in Table 2. Peaks inthe EMG signal were not distinguishable for any fingers except for thethumb, as seen in the plots of EMG signal values for each trial (FIG.5A) and calculated reading-by-reading differences (FIG. 5B). The flexorcarpi radialis summary statistics are listed in Table 3. Peakscorresponding to the motor task were evident for four fingers, as seenin the plots of EMG signal values for each trial (FIG. 6A) andcalculated reading-by-reading differences (FIG. 6B).

TABLE 1 Electromyography summary statistics for the flexor carpi ulnarisBaseline Peak Baseline Peak Difference Difference Finger (mV) (mV) (mV)(mV) SNR Thumb 30.4 ± 5.4 54.6 ± 6.5    0.3 ± 5.3 24.2 ± 6.4  4.6 Index40.4 ± 5.0  61 ± 6.0   0.7 ± 4.4 13.6 ± 2.6  3.1 Middle 39.4 ± 9.7 54.8± 15.4 −0.8 ± 7.3 19.4 ± 10.4 2.7 Ring 41.1 ± 7.0 61.6 ± 7.1  −0.8 ± 5.1 20 ± 5.3 3.9 Little 36.8 ± 8.0 51.8 ± 11.9 −0.5 ± 6.9 16.2 ± 8.8  2.3(average across five trials ± standard deviation)

TABLE 2 Electromyography summary statistics for the brachioradialisBaseline Peak Baseline Peak Difference Difference Finger (mV) (mV) (mV)(mV) SNR Thumb 21.1 ± 4.3 35.4 ± 2.3   0.4 ± 3.1 11.6 ± 4.3 3.7 Index20.5 ± 3.9 N/A   0.1 ± 2.9 N/A N/A Middle 22.1 ± 5.1 N/A −0.5 ± 3.3 N/AN/A Ring 24.6 ± 4.2 N/A   0.1 ± 3.2 N/A N/A Little 21.4 ± 3.8 N/A   0.3± 2.9 N/A N/A (average across five trials ± standard deviation)

TABLE 3 Electromyography summary statistics for the flexor carpiradialis Baseline Peak Baseline Peak Difference Difference Finger (mV)(mV) (mV) (mV) SNR Thumb  9.8 ± 1.7 14.2 ± 1.3   0.1 ± 1.5 3.4 ± 0.5 2.3Index 10.3 ± 2.0 13.2 ± 1.9 −0.2 ± 1.5   4 ± 1.4 2.7 Middle 17.7 ± 3.724.8 ± 5.2   0.1 ± 4.3 6.8 ± 1.8 1.6 Ring 14.6 ± 6.3 25.8 ± 7.0   0.5 ±3.4   10 ± 10.2 2.9 Little 12.4 ± 5.1 N/A   0.2 ± 2.9 N/A N/A (averageacross five trials ± standard deviation)

Discussion/Conclusions: As indicated in the Results, clear EMG readingswere obtained using the setup described in the Methods. Consistent withthe hypotheses, the deliberate muscle activity at the onset of the motortask showed a clearly distinct pattern for the flexor carpi ulnaris andthe flexor carpi radialis, indicated by the peaks seen in FIGS. 4A, 4B,6A, and 6B. By contrast, the motor task onset was not consistentlyevident for the brachioradialis. Interestingly, however, the deliberatemuscle activity at the offset of the task can be observed for thebrachioradialis after 10,000 ms (FIGS. 5A and 5B). Motor activity duringtask offset was not clearly apparent for the flexor carpi ulnaris andwas only partially apparent for the flexor carpi radialis. Also ashypothesized, an optimal target muscle was determined. After havingretrieved data from the three muscles in various locations along theforearm, the flexor carpi ulnaris was determined to output the mostprecise and accurate readings in a multitude of trials across fingers,in that the flexor carpi ulnaris electrode showed a clear activity peakassociated with the onset of the motor task and minimal activityotherwise. Quantitatively, the flexor carpi ulnaris also consistentlyyielded the highest SNR values. While the onset of the motor task wasalso apparent for the flexor carpi radialis, this was less consistentacross trials and more activity was seen at other times throughout thetask. As noted previously, the brachioradialis did not yield adiscernible motor onset peak.

One difficulty in using forearm EMG to control a prosthetic is that allof the muscles that control the fingers are anatomically “deep” (i.e.,toward the bone) while the ones that control the wrist are “superficial”(i.e., closer to the surface). Therefore, a simple surface EMG setupwill necessarily only interface with muscles that control the wrist.However, this experiment indicates that this is not an insurmountableproblem, as consistent surface muscle activity was still seen during thefinger-specific motor task. This is likely due to the way fingers andwrists act together during natural hand motions. Additionally, selectinga muscle that contributes to wrist motion in typical writing ispreferable, as this will make use of the prosthetic feel more natural.The most optimal of the muscles tested was the flexor carpi ulnaris.This muscle has previously been documented to contribute to wristflexion, or bending inward, which is one type of motion the wrist wouldnormally make when writing. For the flexor carpi ulnaris, the Thumbtests had an average SNR of 4.6, nearly 18% greater than the nexthighest SNR for that muscle, which was 3.9 for the Ring finger, andnearly 40% greater than the mean SNR of 3.3 across all five fingers forthe flexor carpi ulnaris. This responsiveness to thumb activity is alsobeneficial for naturalistic prosthetic writing, as the thumb normallycontributes to both holding and precisely moving writing utensils.

The ultimate objective of this experiment and others to follow is toestablish a robust protocol allowing for finger movement uponintentional, biologically controlled activation of a forearm muscleusing a combination of EMG recording and a processing algorithmcurrently in development. This type of setup will mimic the normalbiological process of neuronal finger control, in which muscles areactivated by electrochemical activity at the neuromuscular junction.During muscle activation, the membrane potential of the axon rapidlyincreases (“depolarization”) then decreases (“repolarization”) relativeto the resting membrane potential of approximately −65 mV overapproximately 10 ms. This fluctuation triggers the release ofneurotransmitters at the neuromuscular junction that trigger the muscleto contract. The proposed design will follow a similar scheme in which,upon the EMG signal hitting a threshold, a signal will be sent to motorsin a prosthetic hand to initiate finger motions for writing. Future workwill emphasize 1) development of a reliable control algorithm, 2)testing additional subjects, and 3) developing additional motor tasks tomore accurately reflect natural writing conditions.

Variations and modifications will occur to those of skill in the artafter reviewing this disclosure. The disclosed features may beimplemented, in any combination and subcombination (including multipledependent combinations and subcombinations), with one or more otherfeatures described herein. The various features described or illustratedabove, including any components thereof, may be combined or integratedin other systems. Moreover, certain features may be omitted or notimplemented.

Examples of changes, substitutions, and alterations are ascertainable byone skilled in the art and could be made without departing from thescope of the invention disclosed herein. All references cited herein areincorporated by reference in their entirety and made part of thisapplication.

What is claimed is:
 1. A prosthetic hand for a subject, comprising: ahand body; at least two fingers; a controller that utilizes a neuronalsignal from the subject during a controlled motor activity to determinea threshold, wherein the controller is configured to initiate a motionof at least one finger while gripping and when the threshold has beenexceeded, said motion being different than a movement associated withthe gripping, and wherein the motion comprises painting or brushingteeth using a tool held by the at least one finger including a paintingutensil or a toothbrush, respectively.
 2. The prosthetic hand of claim1, further comprising an electrode configured to be placed on a surfaceof the skin of the subject, wherein the neuronal signal is determinedfrom the electrode.
 3. The prosthetic hand of claim 2, wherein theelectrode is configured to be placed on a forearm.
 4. The prosthetichand of claim 1, wherein the neuronal signal is determined from animplantable device.
 5. The prosthetic hand of claim 1, furthercomprising the tool held by the at least one finger, wherein the motionis a stroke of the tool in response to the neuronal signal having anelevated level that exceeds the threshold.
 6. The prosthetic hand ofclaim 5, wherein the prosthetic hand is further configured to initiatethe motion using a writing utensil.
 7. The prosthetic hand of claim 6,wherein the controller causes the motion by moving the at least onefinger to move between a first, inactivated position to a second,activated position when the threshold has been exceeded.
 8. Theprosthetic hand of claim 7, wherein the controller returns the at leastone finger to the first, inactivated position after the motion iscompleted.
 9. The prosthetic hand of claim 1, wherein the neuronalsignal comprises a combination of neuronal signals, the combination ofneuronal signals being a ratio of a peak neuronal signal of a muscleduring the controlled motor activity to a baseline signal of the musclewithout any motor activity.
 10. The prosthetic hand of claim 9, whereinthe peak neuronal signal is an average peak signal and the baselinesignal is a standard deviation of a previously detected baselineneuronal signal.
 11. The prosthetic hand of claim 1, wherein thecontroller is configured to: determine a baseline neuronal signal of amuscle without any motor activity; determine an elevated neuronal signalof the muscle during the controlled motor activity; compute thethreshold based on a combination of the elevated neuronal signal and thebaseline neuronal signal; discriminate between intentional andunintentional muscle contractions based on the threshold; and facilitateactivation of the prosthetic hand when the threshold has been exceededto effect the motion of the at least one finger during the gripping. 12.A prosthetic appendage for a subject, comprising: an appendage body; atleast one digit; a controller that utilizes a neuronal signal from thesubject during a controlled motor activity to determine a threshold,wherein the controller is configured to initiate a motion of the atleast one digit while gripping and when the threshold has been exceeded,said motion being different than a movement associated with thegripping, and wherein the motion comprises painting or brushing teethusing a tool held by the at least one finger including a paintingutensil or a toothbrush, respectively.
 13. The prosthetic appendage ofclaim 12, wherein the neuronal signal comprises a combination ofneuronal signals, the combination of neuronal signals being a ratio of apeak neuronal signal of a muscle during a controlled motor activity to abaseline signal of the muscle without any motor activity.
 14. A methodof operating a prosthetic hand having a hand body with at least twofingers, comprising: Determining, by an electrode, a baseline neuronalsignal of a muscle of a subject without any motor activity; Determining,by the electrode, an elevated neuronal signal of the muscle during acontrolled motor activity; Computing, by a controller, a threshold basedon a combination of the elevated neuronal signal and the baselineneuronal signal such that discrimination between intentional andunintentional muscle contractions can be determined; and Facilitating,by the controller, activation of the prosthetic hand when the thresholdhas been exceeded to effect a stroking motion of at least one of thefingers on the prosthetic hand while gripping with the at least two ofthe fingers of the hand body, wherein said motion is different than amovement associated with the gripping, and wherein the motion comprisespainting or brushing teeth using a tool held by the at least one fingerincluding a painting utensil or a toothbrush, respectively.
 15. Themethod according to claim 14, further comprising placing the electrodeon a surface of skin of the subject, wherein the baseline neuronalsignal and the elevated neuronal signal are determined by theelectrodes.
 16. The method according to claim 15, wherein the electrodeis configured to be placed on the surface of the skin of a forearm. 17.The method according to claim 14, wherein the baseline neuronal signaland the elevated neuronal signal are determined from an implantabledevice.
 18. The method according to claim 14, wherein the motiongenerates a stroke of the tool.
 19. The method according to claim 18,wherein the tool is a writing utensil wherein the prosthetic hand isfurther configured to initiate the motion using a writing utensil. 20.The method according to claim 19, wherein the activation of theprosthetic hand causes the at least one finger to move between a first,inactivated position to a second, activated position.
 21. The methodaccording to claim 14, further comprising the controller computingsummary statistics of the baseline neuronal signal.
 22. The methodaccording to claim 21, wherein the computing summary statistics of thebaseline neuronal signal consists of computing a standard deviation ofthe baseline neuronal signal.